The authors address the situation in which a researcher wants to cross-tabulate two sets of discrete variables collected in independent samples, but a subset of the variables is common to both samples. The authors propose a statistical data-fusion model that allows for statistical tests of association using multiple imputations. The authors illustrate this approach with an application in which they compare the cross-tabulation results from fused data with those obtained from complete data. Their approach is also compared to the traditional hot-deck procedure.